Combined Compression and Classification with Learning Vector Quantization

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dc.contributor.authorBaras, John S.en_US
dc.contributor.authorDey, Subhrakantien_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T10:05:41Z
dc.date.available2007-05-23T10:05:41Z
dc.date.issued1998en_US
dc.description.abstractCombined compression and classification problems are becoming increasinglyimportant in many applications with large amounts of sensory data andlarge sets of classes. These applications range from aided target recognition(ATR), to medicaldiagnosis, to speech recognition, to fault detection and identificationin manufacturing systems. In this paper, we develop and analyze a learningvector quantization-based (LVQ) algorithm for the combined compressionand classification problem. We show convergence of the algorithm usingtechniques from stochastic approximation, namely, the ODE method. Weillustrate the performance of our algorithm with some examples.en_US
dc.format.extent163945 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5945
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1998-26en_US
dc.subjectdata compressionen_US
dc.subjectsignal processingen_US
dc.subjectPattern Recognitionen_US
dc.subjectLearning Vector Quantizationen_US
dc.subjectStochastic Approximationen_US
dc.subjectIntelligent Signal Processing and Communications Systemsen_US
dc.titleCombined Compression and Classification with Learning Vector Quantizationen_US
dc.typeTechnical Reporten_US

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